Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Reinforcement Schedules

Reinforcement Schedules

Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...

Reinforcement

Reinforcement

Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:

Observational Learning

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...

Dynamic Equilibrium

Dynamic Equilibrium

A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...

Avoidance Learning and Learned Helplessness

Avoidance Learning and Learned Helplessness

Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...

Genetic Drift

Genetic Drift

Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Peripheral and central vestibular neuromodulation improve postural control in adolescent idiopathic scoliosis: a randomized, sham-controlled, multi-arm intervention study.

Journal of neuroengineering and rehabilitation·2026

Same author

scCCVGBen for benchmarking of single-cell representation learning anchored on a centroid-coupled variational graph attention autoencoder across scRNA-seq and scATAC-seq.

Frontiers in genetics·2026

Same author

A Quantum Self-Attention Neural Network Model on Quantum Circuits.

IEEE transactions on cybernetics·2026

Same author

Reduced HAV IgG Seropositivity Among Unvaccinated People Living with HIV: The Weak Shield.

Tropical medicine and infectious disease·2026

Same author

Immunosuppression, resistance burden, and qSOFA on short-term prognosis and difficult clearance in hospitalized patients with Salmonella infection: a single-center retrospective cohort study.

BMC infectious diseases·2026

Same author

LAIOR: a hyperbolic neural ODE variational framework for interpretable single-cell manifold learning and trajectory inference.

Frontiers in genetics·2026

Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026

Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026

Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026

Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026

Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026

Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 28, 2025

New Variations for Strategy Set-shifting in the Rat

New Variations for Strategy Set-shifting in the Rat

Published on: January 23, 2017

Instance Weighted Incremental Evolution Strategies for Reinforcement Learning in Dynamic Environments.

Zhi Wang, Chunlin Chen, Daoyi Dong

IEEE Transactions on Neural Networks and Learning Systems

|March 29, 2022

Summary

This summary is machine-generated.

Evolution strategies (ESs) offer a scalable alternative for reinforcement learning (RL). This study introduces instance-weighted incremental evolution strategies (IW-IESs) for faster adaptation in dynamic environments.

More Related Videos

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

Related Experiment Videos

Last Updated: Sep 28, 2025

New Variations for Strategy Set-shifting in the Rat

New Variations for Strategy Set-shifting in the Rat

Published on: January 23, 2017

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

Area of Science:

Artificial Intelligence
Machine Learning
Optimization Algorithms

Background:

Evolution Strategies (ESs) are scalable black-box optimization algorithms.
ESs offer a faster alternative to Reinforcement Learning (RL) methods, especially with parallel processing.
Adapting learned policies in dynamic environments remains a challenge for existing ES and RL methods.

Purpose of the Study:

To develop a systematic incremental learning method for ES in dynamic environments.
To enable rapid adjustment of previously learned policies to new environmental conditions.
To enhance the adaptability of ES while maintaining its scalability.

Main Methods:

Incorporation of an instance weighting mechanism into ES.
Assigning higher weights to instances with novel information during parameter updating.
Development of instance novelty and instance quality metrics for weight calculation.

Main Results:

The proposed Instance Weighted Incremental Evolution Strategies (IW-IESs) demonstrate significantly improved performance.
IW-IESs achieve superior results on challenging RL tasks, including robot navigation and locomotion.
The method facilitates faster learning adaptation in dynamic environments.

Conclusions:

IW-IESs provide a scalable solution for RL in dynamic environments.
The instance weighting mechanism enhances the adaptation capabilities of ES.
This work introduces a family of ES algorithms for rapid policy adjustment in changing conditions.